{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T22:17:19.603440Z",
     "start_time": "2019-02-12T22:17:19.523397Z"
    }
   },
   "source": [
    "<h1><center> Facial Emotion Recognition - Simple Architecture </center></h1>\n",
    "<center> A project for the French Employment Agency </center>\n",
    "<center> Telecom ParisTech 2018-2019 </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I. Context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this notebook is to explore facial emotion recognition techniques from a live webcam video stream. \n",
    "\n",
    "The data set used for training is the Kaggle FER2013 emotion recognition data set : https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data\n",
    "\n",
    "The models explored include :\n",
    "- Manual filters \n",
    "- Deep Learning Architectures\n",
    "- DenseNet Inspired Architectures\n",
    "\n",
    "This model will be combined with voice emotion recongition as well as psychological traits extracted from text inputs, and should provide a benchmark and a deep analysis of both verbal and non-verbal insights for candidates seeking for a job and their performance during an interview."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# II. General imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Versions used :"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Python : 3.6.5\n",
    "Tensorflow : 1.10.1\n",
    "Keras : 2.2.2\n",
    "Numpy : 1.15.4\n",
    "OpenCV : 4.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:07:47.839370Z",
     "start_time": "2019-03-06T08:07:47.767168Z"
    }
   },
   "outputs": [],
   "source": [
    "### General imports ###\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "from time import sleep\n",
    "import re\n",
    "import os\n",
    "import argparse\n",
    "from collections import OrderedDict\n",
    "import matplotlib.animation as animation\n",
    "\n",
    "### Image processing ###\n",
    "from scipy.ndimage import zoom\n",
    "from scipy.spatial import distance\n",
    "import imutils\n",
    "from scipy import ndimage\n",
    "import cv2\n",
    "import dlib\n",
    "from __future__ import division\n",
    "from imutils import face_utils\n",
    "\n",
    "### CNN models ###\n",
    "import keras\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from keras.callbacks import TensorBoard\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers.convolutional import Conv2D, MaxPooling2D, SeparableConv2D\n",
    "from keras.utils import np_utils\n",
    "from keras.regularizers import l2#, activity_l2\n",
    "from keras.optimizers import SGD, RMSprop\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras import models\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from keras.layers import Input, GlobalAveragePooling2D\n",
    "from keras.models import Model\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "### Build SVM models ###\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import svm\n",
    "\n",
    "### Same trained models ###\n",
    "import h5py\n",
    "from keras.models import model_from_json\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# III. Import datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:07:48.521087Z",
     "start_time": "2019-03-06T08:07:48.517303Z"
    }
   },
   "outputs": [],
   "source": [
    "path = '/Users/maelfabien/filrouge_pole_emploi/Video/'\n",
    "local_path = '/Users/maelfabien/Desktop/LocalDB/Videos/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:07:49.198550Z",
     "start_time": "2019-03-06T08:07:48.778299Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = np.load(local_path + \"X_train.npy\")\n",
    "X_test = np.load(local_path + \"X_test.npy\")\n",
    "y_train = np.load(local_path + \"y_train.npy\")\n",
    "y_test = np.load(local_path + \"y_test.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:07:49.302371Z",
     "start_time": "2019-03-06T08:07:49.277008Z"
    }
   },
   "outputs": [],
   "source": [
    "shape_x = 48\n",
    "shape_y = 48\n",
    "nRows,nCols,nDims = X_train.shape[1:]\n",
    "input_shape = (nRows, nCols, nDims)\n",
    "classes = np.unique(y_train)\n",
    "nClasses = len(classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IV. Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:10:59.688604Z",
     "start_time": "2019-03-06T08:10:59.631086Z"
    }
   },
   "outputs": [],
   "source": [
    "def createModel():\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(64, (3, 3), padding='same', input_shape=(48,48,1)))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Conv2D(128, (3, 3), padding='same'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Conv2D(256, (3, 3), padding='same'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Conv2D(512, (3, 3), padding='same'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2), strides=None, padding='same'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Flatten())\n",
    "\n",
    "    model.add(Dense(512))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Dense(256))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Dense(7))\n",
    "    model.add(Activation('softmax'))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-05T20:05:06.368644Z",
     "start_time": "2019-03-05T20:05:06.321720Z"
    }
   },
   "source": [
    "def createModel():\n",
    "    \n",
    "    #Model Initialization\n",
    "    model = Sequential() \n",
    "    \n",
    "    model.add(Conv2D(20, (3, 3), padding='same', activation='relu', input_shape=input_shape))\n",
    "    model.add(Conv2D(30, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Conv2D(40, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(Conv2D(50, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Conv2D(60, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(Conv2D(70, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Dropout(0.2))\n",
    "    \n",
    "    model.add(Conv2D(80, (3, 3), padding='same', activation='relu'))\n",
    "    model.add(Conv2D(90, (3, 3), padding='same', activation='relu'))\n",
    "    \n",
    "    #Flattening\n",
    "    model.add(Flatten())\n",
    "    \n",
    "    #Adding fully connected layer\n",
    "    model.add(Dense(1000, activation='relu'))\n",
    "    model.add(Dense(512, activation='relu'))\n",
    "    \n",
    "    #Adding Output Layer\n",
    "    model.add(Dense(nClasses, activation='softmax'))\n",
    "     \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:11:03.059836Z",
     "start_time": "2019-03-06T08:11:00.836825Z"
    }
   },
   "outputs": [],
   "source": [
    "model = createModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:11:04.307128Z",
     "start_time": "2019-03-06T08:11:04.295413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_25 (Conv2D)           (None, 48, 48, 64)        640       \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 48, 48, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 48, 48, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling (None, 24, 24, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 24, 24, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_26 (Conv2D)           (None, 24, 24, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 24, 24, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 24, 24, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_27 (Conv2D)           (None, 12, 12, 256)       295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 12, 12, 256)       1024      \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 12, 12, 256)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 6, 6, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 6, 6, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_28 (Conv2D)           (None, 6, 6, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 6, 6, 512)         2048      \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 6, 6, 512)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 512)               2359808   \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "batch_normalization_12 (Batc (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_15 (Dropout)         (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 7)                 1799      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 7)                 0         \n",
      "=================================================================\n",
      "Total params: 4,049,671\n",
      "Trainable params: 4,046,215\n",
      "Non-trainable params: 3,456\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And visualize the model architecture :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:11:08.501910Z",
     "start_time": "2019-03-06T08:11:08.207977Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_model(model, to_file='model_images/model_plot.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='model_images/model_plot.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To prevent overfitting, we can do data augmentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T13:53:44.798652Z",
     "start_time": "2019-02-19T13:53:44.793239Z"
    }
   },
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "        zoom_range=0.2,          # randomly zoom into images\n",
    "        rotation_range=10,       # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        width_shift_range=0.1,   # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=True,    # randomly flip images\n",
    "        vertical_flip=False)     # randomly flip images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-06T08:11:17.011434Z",
     "start_time": "2019-03-06T08:11:16.655357Z"
    }
   },
   "outputs": [],
   "source": [
    "# augment and fit data\n",
    "datagen = ImageDataGenerator(\n",
    "    featurewise_center=False,\n",
    "    samplewise_center=False,\n",
    "    featurewise_std_normalization=False,\n",
    "    samplewise_std_normalization=False,\n",
    "    zca_whitening=False,\n",
    "    rotation_range=0,\n",
    "    width_shift_range=0.0,\n",
    "    height_shift_range=0.0,\n",
    "    shear_range=0,\n",
    "    zoom_range=0.0,\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=False)\n",
    "datagen.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-03-06T08:12:10.336Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "  9/225 [>.............................] - ETA: 27:11 - loss: 2.1947 - acc: 0.2257"
     ]
    }
   ],
   "source": [
    "#Creating 2nd model and training(fitting)\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    " \n",
    "batch_size = 128\n",
    "epochs = 50\n",
    "\n",
    "# Fit the model on the batches generated by datagen.flow().\n",
    "history = model.fit_generator(\n",
    "    datagen.flow(X_train, y_train, batch_size=batch_size),\n",
    "    steps_per_epoch=int(np.ceil(X_train.shape[0] / float(batch_size))),\n",
    "    epochs = epochs, \n",
    "    validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# V. Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-12T22:19:44.845199Z",
     "start_time": "2019-02-12T22:19:43.830788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Curves')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting accuracy and loss curves for 2nd model\n",
    "\n",
    "# Loss Curves\n",
    "plt.figure(figsize=[8,6])\n",
    "plt.plot(history.history['loss'],'r',linewidth=2.0)\n",
    "plt.plot(history.history['val_loss'],'b',linewidth=2.0)\n",
    "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
    "plt.xlabel('Epochs ',fontsize=16)\n",
    "plt.ylabel('Loss',fontsize=16)\n",
    "plt.title('Loss Curves',fontsize=16)\n",
    " \n",
    "# Accuracy Curves\n",
    "plt.figure(figsize=[8,6])\n",
    "plt.plot(history.history['acc'],'r',linewidth=2.0)\n",
    "plt.plot(history.history['val_acc'],'b',linewidth=2.0)\n",
    "plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)\n",
    "plt.xlabel('Epochs ',fontsize=16)\n",
    "plt.ylabel('Accuracy',fontsize=16)\n",
    "plt.title('Accuracy Curves',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VI. Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-19T13:11:45.831737Z",
     "start_time": "2019-02-19T13:11:45.500516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8372"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save the model weights\n",
    "json_string = model.to_json()\n",
    "model.save_weights(local_path + 'savedmodels/model_3.h5')\n",
    "open(local_path + 'savedmodels/model_3.json', 'w').write(json_string)\n",
    "#model.save_weights(local_path + 'savedmodels/Emotion_Face_Detection_Model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-25T14:37:58.450090Z",
     "start_time": "2019-02-25T14:37:57.094325Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "with open(local_path + 'savedmodels/model_3.json','r') as f:\n",
    "    json = f.read()\n",
    "model = model_from_json(json)\n",
    "\n",
    "model.load_weights(local_path + 'savedmodels/model_3.h5')\n",
    "print(\"Loaded model from disk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VII. Sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Visualization : https://github.com/JostineHo/mememoji/blob/master/data_visualization.ipynb\n",
    "- State of the art Architecture : https://github.com/amineHorseman/facial-expression-recognition-using-cnn\n",
    "- Eyes Tracking : https://www.pyimagesearch.com/2017/04/24/eye-blink-detection-opencv-python-dlib/\n",
    "- Face Alignment : https://www.pyimagesearch.com/2017/05/22/face-alignment-with-opencv-and-python/\n",
    "- C.Pramerdorfer,  and  M.Kampel.Facial  Expression  Recognition  using  Con-volutional  Neural  Networks:  State  of  the  Art.  Computer  Vision  Lab,  TU  Wien. https://arxiv.org/pdf/1612.02903.pdf\n",
    "- A Brief Review of Facial Emotion Recognition Based\n",
    "on Visual Information : https://www.mdpi.com/1424-8220/18/2/401/pdf\n",
    "- Going deeper in facial expression recognition using deep neural networks : https://ieeexplore.ieee.org/document/7477450"
   ]
  }
 ],
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